WO2019028618A1 - 基于大数据的商标价值评估的方法及系统 - Google Patents

基于大数据的商标价值评估的方法及系统 Download PDF

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WO2019028618A1
WO2019028618A1 PCT/CN2017/096284 CN2017096284W WO2019028618A1 WO 2019028618 A1 WO2019028618 A1 WO 2019028618A1 CN 2017096284 W CN2017096284 W CN 2017096284W WO 2019028618 A1 WO2019028618 A1 WO 2019028618A1
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trademark
evaluated
value
big data
report
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PCT/CN2017/096284
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English (en)
French (fr)
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万忠凯
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深圳益强信息科技有限公司
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Priority to PCT/CN2017/096284 priority Critical patent/WO2019028618A1/zh
Publication of WO2019028618A1 publication Critical patent/WO2019028618A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/18—Legal services; Handling legal documents

Definitions

  • the invention relates to a method and a system for evaluating a trademark value, in particular to a method and a system for evaluating a trademark value based on big data.
  • trademarks have become the most important shopping orientation, and become a bridge between enterprises and consumers.
  • Enterprises with high-value trademarks mean possession of the market. China has become a major trademark country, and the number of trademark registrations has reached the highest in the world. It can be seen that trademarks are gaining more and more attention, and their value is increasingly recognized by people.
  • Trademark transactions are becoming more frequent, but The value evaluation of traditional trademarks mainly adopts manual methods. There are too many subjective factors, and there are few considerations on market value. Trademark holders or purchasers cannot objectively and comprehensively understand and locate the basic value of trademarks.
  • the object of the present invention is to provide a method and system for evaluating the value of a trademark based on big data by objectively and comprehensively understanding and locating the basic value of the trademark by analyzing the big data.
  • the present invention adopts the following technical means:
  • a method for evaluating a trademark value based on big data comprising the following steps: S1: preparing basic information of a trademark to be evaluated: a trademark name and a series of design elements of the trademark to be evaluated; S2: basic information input of the trademark to be evaluated: The basic information of the trademark to be evaluated in S1 is entered into the system; S3: The value of the trademark is judged according to the big data: S3.1: The dimension is established, including the infringement trial, the transaction amount of the same or similar category trademark, the authorization time, and the trademark of the category.
  • the method for judging the similar type of trademark in S3.1, the graphic to be evaluated is: extracting the graphic of the trademark to be evaluated, and determining whether the same previously registered trademark or the trademark being applied in the same subcategory If yes, determine whether the previously registered trademark or the trademark being applied for is valid. If it is valid, judge whether it is similar to the commodity. If it is similar, give a suggestion that the application is unlikely to be successful and generate a report. If it is not similar, it will give a suggestion that the application is highly likely to be successful and generate a report. If it is invalid, it will judge whether it exceeds the predetermined time. If it is exceeded, it will give a suggestion that the possibility of successful application is low and generate a report. If it is not exceeded, then Give suggestions for the possibility of successful application and generate a report. If not, determine whether the trademark to be evaluated is similar to the previously registered trademark or the trademark being applied for.
  • phase approximation is judged by the artificial intelligence module.
  • the method for judging the artificial intelligence module is: first inputting the graphic, and then analyzing the basic constituent elements, wherein the elements are based on the graphic elements of the trademark, and analyzing the composition of each basic element according to the historical review data, for all the classifications
  • the results of the rules are weighted in a certain way, which can be adjusted according to the applicant's risk preferences.
  • the judgment of the non-approximation is judged by the big data module.
  • the method for judging the big data module is: classifying according to the grammar rules, each classification rule is judged according to the historical review data, and the results of all the classification rules are weighted according to a predetermined manner, and the weighted values can be applied according to the application. People's risk preferences are adjusted.
  • the same is determined based on whether the image coincidence degree exceeds a predetermined value.
  • the determination of the image coincidence degree includes judging by the coincidence of any angular rotation.
  • the method for judging the similar type of trademark in S3.1, the trademark to be evaluated is: extracting the text of the trademark to be evaluated, and determining whether the same previously registered trademark or the trademark being applied in the same subcategory The same judgment is judged in accordance with the provisions of the Trademark Law, the Implementation Rules and the Examination Guidelines. If so, it is judged whether the previously registered trademark or the trademark being applied for is valid. If it is valid, it is judged whether it is similar to the commodity. Approximate, give a suggestion that the application is unlikely to be successful and generate a report. If it is not approximate, give a suggestion that the application is highly likely to be successful and generate a report. If it is invalid, judge whether it exceeds the predetermined time.
  • the method for judging the big data module is: classifying according to the grammar rules, each classification rule is judged according to the historical review data, and the results of all the classification rules are weighted according to a predetermined manner, and the weighted values can be applied according to the application. People's risk preferences are adjusted.
  • the dimension established in S3.1 further includes the number of listed companies in the industry and the profitability of listed companies in the industry.
  • the dimension established in S3.1 further includes the number and rate of registered companies in the industry, the number and rate of e-commerce.
  • the infringement trial in S3.1 is to extract the previously registered trademark of the commodity involved in the infringement dispute or the category, city, country or region of the trademark being applied for.
  • weights in S3.2 can be dynamically adjusted, and the weight adjustment can be made for emerging industries.
  • the emerging industry has three criteria: the first is the number of companies registered in the field, and the second is the number of companies financing in the field. The third is the number of companies registered in the field and the amount of financing.
  • the basic information input of the trademark to be evaluated in S2 is entered into the system by the input module, the information of the dimension established in S3.1 is derived from the database, and the information in S3.2 and S3.3 is analyzed by the big data judgment module and the artificial intelligence module.
  • the value evaluation report of the trademark to be evaluated in S4 is output by the output module.
  • a system for evaluating the trademark value based on big data comprising: an input module for inputting basic information of the trademark to be evaluated, and the basic information of the trademark to be evaluated is a brand name and a series of design elements;
  • a database obtains the data of the dimension from the database, and the data of the dimension includes the infringement trial, the transaction amount of the same or similar category trademark, the authorization time, the total number of trademarks of the category, the rejection rate; a large data judgment module and a
  • the artificial intelligence module analyzes the value of the trademark according to the big data, assigns a certain weight to each variable related to the transaction amount, and then weights the basic value, and weights each variable irrelevant to the transaction amount to form a factor 1 with a coefficient greater than 1.
  • the number of similar trademarks forms a factor of less than 1 and the value of the trademark to be evaluated is equal to the basic value * factor 1 * factor 2; an output module outputs the final value evaluation report of the trademark to be evaluated.
  • extracting a graphic of the trademark to be evaluated determining whether the same previously registered trademark or the trademark being applied for in the same subclass, and if so, judging whether the previously registered trademark or the trademark being applied for is Valid, if it is valid, it is judged whether it is similar to the commodity. If it is approximate, it gives a suggestion that the application is unlikely to be successful and generates a report. If it is not approximate, it gives a suggestion that the application is highly likely to be successful and generates a report. If it is invalid, it will judge whether it exceeds the predetermined time. If it exceeds, it will give a suggestion that the possibility of successful application is low and generate a report.
  • the words of the trademark to be evaluated are extracted, and it is judged whether the same previously registered trademark or the trademark being applied for in the same sub-category, and the same judgment is judged according to the provisions of the trademark law, the implementation rules and the examination guide. If yes, it is judged whether the previously registered trademark or the trademark being applied for is valid. If it is valid, it is judged whether it is similar to the commodity. If it is similar, it gives a suggestion that the application is unlikely to be successful and generates a report, if not , giving a recommendation that the application is highly likely to be successful and generating a report. If it is invalid, it is judged whether it exceeds the predetermined time.
  • the establishment dimension further includes the number of listed companies in the industry and the profitability of listed companies in the industry.
  • the establishment dimension further includes the number and rate of registered companies in the industry, the number and rate of e-commerce.
  • the infringement trial is to extract the previously registered trademark of the commodity involved in the infringement dispute or the type, city, country or region of the trademark being applied for, the previously registered trademark of the infringement dispute or the trademark being applied for. The amount of money and the parties to the dispute.
  • weights can be dynamically adjusted, and the weights can be adjusted for emerging industries.
  • the emerging industry has three criteria: the first is the number of companies registered in the field, the second is the number of companies financing in the field, and the third is Number of company registrations and financing in this area.
  • the present invention has the following beneficial effects:
  • the above-mentioned methods and systems for evaluating the value of trademarks based on big data are analyzed by the use of big data to evaluate the value of trademarks, specifically to establish dimensions, including infringement trials, transaction amounts of the same or similar categories of trademarks, authorization time, and trademarks of this category.
  • the total quantity and rejection rate are given a certain weight for each variable related to the transaction amount, and then weighted to obtain the basic value.
  • the weight formation coefficient is greater than 1 and the number formation coefficient of the similar trademark is smaller than Factor 2 of 1, the value of the trademark to be evaluated is equal to the basic value * factor 1 * factor 2, so that the output is to be evaluated
  • the result reflects the market economy value, has a high reference value, and the result is more reference value, and the system automatically collects and compares, saves a lot of manpower, and facilitates subsequent monitoring of similar trademarks.
  • FIG. 1 is a general flow chart of a method for evaluating a trademark value based on big data according to the present invention
  • FIG. 2 is a general structural diagram of a system for evaluating a trademark value based on big data according to the present invention.
  • a method for valuation of a trademark based on big data includes the following steps:
  • S1 Basic information for preparing the trademark to be evaluated: the trademark name and design element series of the trademark to be evaluated, including the content of the series of elements, the content of the text, the information to be highlighted in the pattern, the color, the style, and the like.
  • S2 Basic information entry of the trademark to be evaluated: the basic information of the trademark to be evaluated in S1 is correspondingly entered into the system;
  • S3.1 Establish dimensions, including infringement trials, transaction amounts of the same or similar categories of trademarks, authorization time, the total number of trademarks in the category, and the rejection rate. This information is the most basic and is obtained in the field of peers. The process of acquisition needs to go through multiple channels and ultimately be confirmed manually.
  • the method for judging the similar type of trademark in S3.1, the graphic to be evaluated is: extracting the graphic of the trademark to be evaluated, and judging whether the same previously registered trademark or the trademark being applied in the same subclass, For example, the trademarks to be evaluated belong to 35 categories of advertising, business management, business management, etc., but 35 categories belong to the general category.
  • the main sales model of the products corresponding to the trademark to be evaluated is the pay-per-click advertisement, and the pay-per-click advertisement is 350113. Look further There are no identical previously registered trademarks or trademarks under application in the 350113 category.
  • the predetermined value of the image coincidence degree is set to 60%, and if the value of the image coincidence degree is 80%, the predetermined value is exceeded, so that the application is successful. Suggestions with low probability and report generation. If the value of image coincidence is 40%, then the predetermined value is not exceeded. This gives a high probability of successful application and generates a report.
  • the determination of the image coincidence includes judging by the coincidence of any angular rotation. For example, one of the previously registered trademarks or the trademark being applied is vertically placed, and the pattern in the trademark to be evaluated is inclined by 30 degrees. If the angle is placed, then the judgment of the image coincides with the rotation angle.
  • the image is coincident after 30 degrees of rotation, if the value of the image coincidence value exceeds 60% of the predetermined value, the recommendation that the application success probability is low will be given.
  • the report is generated. Conversely, if the predetermined value is not exceeded, the proposal for high probability of successful application is generated and the report is generated. This breaks through the difficulty of the graphic to be evaluated, and no omission occurs, and the risk coefficient is low.
  • the phase approximation is judged by the artificial intelligence module.
  • the method of judging the artificial intelligence module is as follows: first inputting the graph, then analyzing the basic constituent elements, the elements are based on the graphic elements of the trademark, and analyzing the composition schemes of the basic elements according to the historical review data, and the results of all the classification rules
  • the weighting calculation is performed in a certain way, and the weighting value can be adjusted according to the applicant's risk preference. For example, if the figure is a running horse, the horse's graphic is input first, and then the basic components of the horse, such as a horse, are analyzed.
  • the horse's demeanor was particularly exaggerated and anger. This point is significant, so the 2016 trademark was also authorized, so that the reasons for authorization and non-authorization can be carried out.
  • Classification management forming certain classification rules, and assigning different weights, and finally calculating according to the weights, and obtaining a reasonable plan.
  • the weighting value can be adjusted according to the applicant's risk preference, that is, if the weighting value analyzed by the artificial intelligence module is unsatisfactory, the applicant can make appropriate adjustments, such as the weighted value analyzed by the artificial intelligence module. 70.
  • the applicant can adjust the weighting value to 80.
  • the judgment of the non-approximation is judged by the big data module.
  • the method for judging the big data module is: classifying according to the grammar rules, each classification rule is judged according to the historical review data, and the results of all the classification rules are weighted according to a predetermined manner, and the weighted values can be based on the risk of the applicant.
  • the preference adjustment, the grammar rules are analyzed by the big data module, and judged according to the previous historical review data to form different classification rules.
  • the method for judging the similar category trademark in S3.1, the trademark to be evaluated is: extracting the text of the trademark to be evaluated, and judging whether the same previously registered trademark or the trademark being applied in the same subclass, The same judgment is judged according to the provisions of the Trademark Law, the Implementation Rules and the Examination Guidelines. If so, it is judged whether the previously registered trademark or the trademark being applied for is valid. If it is valid, it is judged whether it is similar to the commodity, if it is similar , give a suggestion that the possibility of successful application is low and generate a report. If it is not approximate, give a suggestion that the application is highly likely to be successful and generate a report. If it is invalid, it is judged whether it exceeds the predetermined time.
  • the judgment of the non-approximation and the judgment of the similarity are all judged by the big data module.
  • the method for judging the big data module is: classifying according to the grammar rules, each classification rule is judged according to the historical review data, and the results of all the classification rules are weighted according to a predetermined manner, and the weighted values can be based on the risk of the applicant. Like to adjust.
  • the dimension established in S3.1 further includes the number of listed companies in the industry and the profitability of listed companies in the industry. For example, the number of listed companies in the industry is 1,000, and the average profit margin of listed companies in the industry is 20% to 25 %, then it can be seen that the profit rate of this industry is still relatively objective.
  • the establishment of dimensions in S3.1 further includes the number and rate of registered companies in the industry, the number and rate of e-commerce, and thus conforms to the trend of the times, so that the value of the final trademark can be maximized, and the subsequent establishment of the well-known trademark also lays the foundation.
  • the infringement trial in S3.1 is the pre-registered trademark of the infringement dispute or the type, city, country or region of the trademark being applied for, and the previously registered trademark or in the application for infringement dispute.
  • S3.2 assign a certain weight to each variable related to the transaction amount, and then weight the basic value.
  • the weight of the company type related to the transaction amount is 10%
  • the weight of the foreign trade of the commodity is 20%
  • the weight of the rate is 40%
  • the weight of the company's total business income is 30%
  • the information is obtained from the database.
  • the basic value of the weight is 22.14.
  • the weighting factor 1 is greater than 1, and the number of similar trademarks forms a factor 2 with a coefficient less than 1.
  • the transaction-independent variables include the company's personnel structure, the company's established area, and the company's survival life.
  • the weighted value forming coefficient formed by these variables has a factor of 1.8, so the factor 1 is greater than 1, and the factor 2 of the number forming coefficient of the similar trademark is 0.6, so the factor 2 is less than 1, such a measurement method gives weights to different layers.
  • the weighted value forming coefficient formed by these variables has a factor of 1.8, so the factor 1 is greater than 1
  • the factor 2 of the number forming coefficient of the similar trademark is 0.6
  • the factor 2 is less than 1
  • the weights in S3.2 can be dynamically adjusted, and the weight adjustment can be made for emerging industries.
  • the emerging industry has three criteria: the first is the number of companies registered in the field, and the second is the number of companies financing in the field.
  • the third type is the number of companies registered in this field and the amount of financing. This is a common development model in the future. In this regard, it is more responsive to the trend of the times and is conducive to the subsequent development of the company.
  • a system for evaluating the trademark value based on big data includes: an input module for inputting basic information of a trademark to be evaluated, and basic information of the trademark to be evaluated is a brand name and a series of design elements;
  • the database obtains the data of the dimension from the database, and the data of the dimension includes the infringement trial, the transaction amount of the same or similar category trademark, the authorization time, the total number of trademarks of the category, the rejection rate; a large data judgment module and an artificial
  • the intelligent module analyzes the value of the trademark according to the big data, assigns a certain weight to each variable related to the transaction amount, and then weights the basic value, and weights each variable irrelevant to the transaction amount to form a factor of 1 greater than 1, similar to The number of trademarks forms a factor of less than 1 and the value of the trademark to be evaluated is equal to the basic value * factor 1 * factor 2; an output module outputs the final value assessment report of the trademark to be evaluated.
  • the basic information input of the trademark to be evaluated in the above S2 is entered into the system by the input module, the information of the dimension established in S3.1 is derived from the database, and the information in S3.2 and S3.3 is analyzed by the big data judgment module and the artificial intelligence module.
  • the value evaluation report of the trademark to be evaluated in S4 is output by the output module.

Abstract

一种基于大数据的商标价值评估的方法及系统，其包括以下步骤：S1：准备待评估商标的基本信息；S2：待评估商标的基本信息录入；S3：根据大数据判断商标的价值：S3.1：建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，S3.2：对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，S3.3：待评估商标价值等于基本值*因子1*因子2；S4：输出待评估商标的价值评估报告，以达到对商标的基本价值进行客观、全面了解以及定位的目的。

Description

基于大数据的商标价值评估的方法及系统 【技术领域】
本发明涉及一种商标价值评估的方法及系统，尤指一种基于大数据的商标价值评估的方法及系统。
【背景技术】
随着经济的发展，在日益激烈的市场竞争中,商标已成为最重要的购物导向,成为联系企业和消费者的桥梁，企业拥有价值高的商标,便意味着对市场的占有。中国已经成为商标大国，商标的注册量已经达到了世界第一，由此可以看出，商标越来越得到重视，价值也越来越被人们所认可，商标交易现象也越来越频繁，可是传统商标的价值评估主要采用人工方式，有过多的主观因素，对于市场价值的考虑较少，商标持有人或购买人没法对商标的基本价值进行客观和全面的了解以及定位。
因此，有必要设计一种好的基于大数据的商标价值评估的方法及系统，以克服上述问题。
【发明内容】
针对背景技术所面临的问题，本发明的目的在于提供一种通过对大数据进行分析，以达到对商标的基本价值进行客观、全面了解以及定位的基于大数据的商标价值评估的方法及系统。
为实现上述目的，本发明采用以下技术手段：
一种基于大数据的商标价值评估的方法，其包括以下步骤：S1：准备待评估商标的基本信息：待评估商标的商标名称和设计要素系列资料；S2：待评估商标的基本信息录入： 将S1中待评估商标的基本信息对应录入系统；S3：根据大数据判断商标的价值：S3.1：建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，S3.2：对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，S3.3：待评估商标价值等于基本值*因子1*因子2；S4：输出待评估商标的价值评估报告：上述步骤全部完成后，形成最终的价值评估报告。
进一步地，S3.1中相近似类别商标的判断方法，图形的待评估商标为：提取待评估商标的图形，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，如果有,则判断在先已注册的商标或正在申请中的商标是否有效，如果是有效,则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似,则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似。
进一步地，所述相近似的判断由人工智能模块进行判断。
进一步地，人工智能模块的判断方法为：首先将图形输入，接着分析基本组成要素，所述要素以商标的图形要素为基本要素，分析各个基本要素组成方案按照历史审查数据进行判断，对所有分类规则的结果按照一定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整。
进一步地，所述不近似的判断由大数据模块进行判断。
进一步地，大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整。
进一步地，所述相同基于影像重合度是否超过预定值进行判断。
进一步地，所述影像重合度的判断包括任意角度旋转的重合进行判断。
进一步地，S3.1中相近似类别商标的判断方法，文字的待评估商标为：提取待评估商标的文字，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，所述相同的判断按照商标法、实施细则及审查指南规定进行判断，如果有，则判断在先已注册的商标或正在申请中的商标是否有效，如果有效，则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似，则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似。
进一步地，所述不近似的判断和所述相近似的判断均由大数据模块进行判断。
进一步地，大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整。
进一步地，S3.1中建立维度进一步包括该行业上市公司的数量和该行业上市公司的利润率。
进一步地，S3.1中建立维度进一步包括该行业注册公司的数量及速率、电商的数量及速率。
进一步地，S3.1中侵权审判为提取该商品涉及侵权纠纷的在先已注册的商标或正在申请中的商标的类别、城市、国家或地区。
进一步地，涉及侵权纠纷的在先已注册的商标或正在申请中的商标的金钱数额和纠纷当事人。
进一步地，S3.2中的权重可动态调整，对新兴行业可以进行权重调整，新兴行业判断标准为三种：第一种为该领域的公司注册数量，第二种为该领域的公司融资数量，第三种为该领域的公司注册数量和融资数量。
进一步地，S2中待评估商标的基本信息录入是由输入模块录入系统，S3.1中建立维度的信息来源于数据库，S3.2和S3.3中信息由大数据判断模块和人工智能模块分析，S4中的待评估商标的价值评估报告由输出模块输出。
另一技术方案为：一种基于大数据的商标价值评估的系统，其包括：一输入模块，将待评估商标的基本信息进行录入，待评估商标的基本信息为商标名称和设计要素系列资料；一数据库，从数据库中获取建立维度的资料，建立维度的资料包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率；一大数据判断模块和一人工智能模块，分析出根据大数据判断商标的价值，对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，待评估商标价值等于基本值*因子1*因子2；一输出模块，将待评估商标的最终价值评估报告输出。
进一步地，提取待评估商标的图形，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，如果有,则判断在先已注册的商标或正在申请中的商标是否有效，如果是有效,则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似,则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似，所述相近似的判断由人工智能模块进行判断，所述不近似的判断由大数据模块进行判断。
进一步地，提取待评估商标的文字，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，所述相同的判断按照商标法、实施细则及审查指南规定进行判断，如果有，则判断在先已注册的商标或正在申请中的商标是否有效，如果有效，则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似，则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似，所述不近似的判断和所述相近似的判断均由大数据模块进行判断。
进一步地，建立维度进一步包括该行业上市公司的数量和该行业上市公司的利润率。
进一步地，建立维度进一步包括该行业注册公司的数量及速率、电商的数量及速率。
进一步地，侵权审判为提取该商品涉及侵权纠纷的在先已注册的商标或正在申请中的商标的类别、城市、国家或地区，涉及侵权纠纷的在先已注册的商标或正在申请中的商标的金钱数额和纠纷当事人。
进一步地，权重可动态调整，对新兴行业可以进行权重调整，新兴行业判断标准为三种：第一种为该领域的公司注册数量，第二种为该领域的公司融资数量，第三种为该领域的公司注册数量和融资数量。
与现有技术相比，本发明具有以下有益效果：
上述基于大数据的商标价值评估的方法及系统，由于采用大数据对待评估商标的价值进行分析，具体为建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，待评估商标价值等于基本值*因子1*因子2，这样输出待评 估商标最终的价值评估报告，结果反映了市场经济价值，具有较高的参考意义，结果也就更具参考价值，并且系统自动采集对比,节省了大量人力，也便于后续类似商标的监控。
【附图说明】
图1为本发明基于大数据的商标价值评估的方法的总体流程图；
图2为本发明基于大数据的商标价值评估的系统的总体结构图。
【具体实施方式】
为便于更好的理解本发明的目的、结构、特征以及功效等，现结合附图和具体实施方式对本发明作进一步说明。
请参见图1，一种基于大数据的商标价值评估的方法，其包括以下步骤：
S1：准备待评估商标的基本信息：待评估商标的商标名称和设计要素系列资料，其中涉及要素系列资料包括文字的内容、图案中需要凸显的信息、颜色、风格等。
S2：待评估商标的基本信息录入：将S1中待评估商标的基本信息对应录入系统；
S3：根据大数据判断商标的价值：
进一步，S3.1：建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，这些信息是最基础的，在同行的领域中进行获取，获取的过程需要经过多种渠道，并且最终还要由人工进行确认。
其中，S3.1中相近似类别商标的判断方法，图形的待评估商标为：提取待评估商标的图形，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，例如，待评估商标属于广告、商业经营、商业管理等的35类，但35类属于大类，待评估商标对应的商品主要的销售模式是通过点击付费的广告，而点击付费广告是350113类，就进一步看一 下350113类中有没有相同的在先已注册的商标或正在申请中的商标。
如果有,则判断在先已注册的商标或正在申请中的商标是否有效，如果是有效,则判断与商品是否近似，有效指的是法律状态是否有效，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似,则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似。所述相同基于影像重合度是否超过预定值进行判断，例如，影像重合度的预定值设为60％，如果影像重合度的数值为80％，那么就超过了预定值，这样则给出申请成功可能性低的建议并生成报告，如果影像重合度的数值为40％，那么就没有超过了预定值，这样则给出申请成功可能性高的建议并生成报告。所述影像重合的判断包括任意角度旋转的重合进行判断，例如，在先已注册的商标或正在申请中的商标中的一个图案是竖直摆放，而待评估商标中的图案是倾斜30度角的摆放，那么所述影像重合的判断会旋转角度，旋转30度后如果重合，影像重合度的数值只要超过预定值的60％，那么就还是会给出申请成功可能性低的建议并生成报告，反之，没有超过了预定值，则给出申请成功可能性高的建议并生成报告，这样突破了图形的待评估商标的难点，不会发生遗漏，风险系数低。
所述相近似的判断由人工智能模块进行判断。人工智能模块的判断方法为：首先将图形输入，接着分析基本组成要素，所述要素以商标的图形要素为基本要素，分析各个基本要素组成方案按照历史审查数据进行判断，对所有分类规则的结果按照一定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整，例如，图形为一匹奔跑中的马，就先将马的图形输入，接着分析马的基本组成要素，比如马的颜色、马的大小、马的轮廓、马的品种以及对马身上的装饰品等，之后根据各个基本要素形成的马的图形方案，按照之 前类似的历史审查数据进行判断，比如2013年有一个商标申请了一匹小马，成功授权，2014年有一个商标申请了一匹马，与2013年商标不同的是，马的大小和颜色不一样，其余轮廓和马的神态都一样，所以2014年的商标被驳回，没有授权，但2015年有一个商标申请的是马以及马背上的座垫的组合，这样2015年的商标被授权了，2016年有一个商标申请的是马以及马的神态，马的神态特别的夸张愤怒，该点具有显著性，故2016年的商标也被授权了，这样就可以对授权和不授权的理由进行分类管理，形成一定的分类规则，并且赋予不同的权重，最后根据加权来计算，得出合理的方案。其中，所述加权值可以根据申请人的风险喜好调整，就是如果人工智能模块分析出来的加权值如果申请人不满意，申请人可以做出适当的调整，比如人工智能模块分析出来的加权值为70，申请人可以将加权值调整为80。
所述不近似的判断由大数据模块进行判断。大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整，语法规则就是大数据模块分析出来的，根据之前的历史审查数据进行判断，形成不同的分类规则。
其中，S3.1中相近似类别商标的判断方法，文字的待评估商标为：提取待评估商标的文字，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，所述相同的判断按照商标法、实施细则及审查指南规定进行判断，如果有，则判断在先已注册的商标或正在申请中的商标是否有效，如果有效，则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似，则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似。
所述不近似的判断和所述相近似的判断均由大数据模块进行判断。大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整。
此外，S3.1中建立维度进一步包括该行业上市公司的数量和该行业上市公司的利润率，例如，该行业上市公司的数量为1000家，该行业上市公司平均的利润率为20％至25％，那么可以看出这个行业的利润率还是比较客观的。S3.1中建立维度进一步包括该行业注册公司的数量及速率、电商的数量及速率，如此顺应时代的潮流，这样最终商标的价值才能发挥到最大，后续对获取驰名商标也奠定了基础。S3.1中侵权审判为提取该商品涉及侵权纠纷的在先已注册的商标或正在申请中的商标的类别、城市、国家或地区，涉及侵权纠纷的在先已注册的商标或正在申请中的商标的金钱数额和纠纷当事人，如此避免后续发展中出现纠纷，影响商标的使用，进一步让企业受损。
进一步，S3.2：对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，例如，交易金额相关的公司类型的权重为10％，商品对外贸易的权重为20％，公司利润率的权重为40％，公司总营业务收入的权重为30％，从数据库的信息获取，权为100个10，60个20，40个40,80个30，根据加权的计算方法，(10*100+20*60+40*40+30*80)/(100+60+40+80)＝22.14,得出加权的基本值为22.14。对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，例如，交易金额无关的变量有公司人员架构、公司设立的区域、公司的存活寿命等，这些变量形成的加权值形成系数的因子1为1.8,故因子1大于1，相似商标的数量形成系数的因子2为0.6，故因子2小于1，这样的测定方法，对不同层面赋予权重，以判别各差异，配合加权的使用，不会单纯看一个数据，而是综合考虑，这样得出来的结论是比较理智和客观的，风险系数小。
其中，S3.2中的权重可动态调整，对新兴行业可以进行权重调整，新兴行业判断标准为三种：第一种为该领域的公司注册数量，第二种为该领域的公司融资数量，第三种为该领域的公司注册数量和融资数量，这是以后商业中常见的发展模式，朝这方面考虑，更能顺应时代的潮流，利于企业后续的发展。
进一步，S3.3：待评估商标价值等于基本值*因子1*因子2，根据前面举出的例子，待评估商标价值等于22.14*1.8*0.6＝23.9112，综合考虑，不仅考虑跟交易金额相关的各变量，同时也会考虑跟交易金额无关的各变量，这样得出来的结论更客观，风险系数当然也就更小，能尽量做到心中有数，不受主观因素的干扰。
S4：输出待评估商标的价值评估报告：上述步骤全部完成后，形成最终的价值评估报告。
请参见图2，一种基于大数据的商标价值评估的系统，其包括：一输入模块，将待评估商标的基本信息进行录入，待评估商标的基本信息为商标名称和设计要素系列资料；一数据库，从数据库中获取建立维度的资料，建立维度的资料包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率；一大数据判断模块和一人工智能模块，分析出根据大数据判断商标的价值，对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，待评估商标价值等于基本值*因子1*因子2；一输出模块，将待评估商标的最终价值评估报告输出。故上述S2中待评估商标的基本信息录入是由输入模块录入系统，S3.1中建立维度的信息来源于数据库，S3.2和S3.3中信息由大数据判断模块和人工智能模块分析，S4中的待评估商标的价值评估报告由输出模块输出。
请参见图1，上述基于大数据的商标价值评估的方法及系统，由于采用大数据对待评 估商标的价值进行分析，具体为建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，待评估商标价值等于基本值*因子1*因子2，这样输出待评估商标最终的价值评估报告，结果反映了市场经济价值，具有较高的参考意义，结果也就更具参考价值，并且系统自动采集对比,节省了大量人力，也便于后续类似商标的监控。
以上详细说明仅为本发明之较佳实施例的说明，非因此局限本发明的专利范围，所以，凡运用本创作说明书及图示内容所为的等效技术变化，均包含于本发明的专利范围内。

Claims (10)

  1. 一种基于大数据的商标价值评估的方法，其特征在于，包括以下步骤：
    S1：准备待评估商标的基本信息：待评估商标的商标名称和设计要素系列资料；
    S2：待评估商标的基本信息录入：将S1中待评估商标的基本信息对应录入系统；
    S3：根据大数据判断商标的价值：
    S3.1：建立维度，包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率，
    S3.2：对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，
    S3.3：待评估商标价值等于基本值*因子1*因子2；
    S4：输出待评估商标的价值评估报告：上述步骤全部完成后，形成最终的价值评估报告。
  2. 如权利要求1所述的基于大数据的商标价值评估的方法，其特征在于：S3.1中相近似类别商标的判断方法，图形的待评估商标为：提取待评估商标的图形，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，如果有,则判断在先已注册的商标或正在申请中的商标是否有效，如果是有效,则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似,则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似。
  3. 如权利要求2所述的基于大数据的商标价值评估的方法，其特征在于：所述相近似的判 断由人工智能模块进行判断；人工智能模块的判断方法为：首先将图形输入，接着分析基本组成要素，所述要素以商标的图形要素为基本要素，分析各个基本要素组成方案按照历史审查数据进行判断，对所有分类规则的结果按照一定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整；所述不近似的判断由大数据模块进行判断。
  4. 如权利要求3所述的基于大数据的商标价值评估的方法，其特征在于：大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整；所述相同基于影像重合度是否超过预定值进行判断；所述影像重合度的判断包括任意角度旋转的重合进行判断。
  5. 如权利要求1所述的基于大数据的商标价值评估的方法，其特征在于：S3.1中相近似类别商标的判断方法，文字的待评估商标为：提取待评估商标的文字，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，所述相同的判断按照商标法、实施细则及审查指南规定进行判断，如果有，则判断在先已注册的商标或正在申请中的商标是否有效，如果有效，则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似，则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似；所述不近似的判断和所述相近似的判断均由大数据模块进行判断；大数据模块的判断方法为：根据语法规则进行分类，每个分类规则,按照历史审查数据进行判断，对所有分类规则的结果按照预定的方式进行加权计算，所述加权值可以根据申请人的风险喜好调整。
  6. 如权利要求1所述的基于大数据的商标价值评估的方法，其特征在于：S3.1中建立维 度进一步包括该行业上市公司的数量和该行业上市公司的利润率；S3.1中建立维度进一步包括该行业注册公司的数量及速率、电商的数量及速率；S3.1中侵权审判为提取该商品涉及侵权纠纷的在先已注册的商标或正在申请中的商标的类别、城市、国家或地区；涉及侵权纠纷的在先已注册的商标或正在申请中的商标的金钱数额和纠纷当事人；S3.2中的权重可动态调整，对新兴行业可以进行权重调整，新兴行业判断标准为三种：第一种为该领域的公司注册数量，第二种为该领域的公司融资数量，第三种为该领域的公司注册数量和融资数量；S2中待评估商标的基本信息录入是由输入模块录入系统，S3.1中建立维度的信息来源于数据库，S3.2和S3.3中信息由大数据判断模块和人工智能模块分析，S4中的待评估商标的价值评估报告由输出模块输出。
  7. 一种基于大数据的商标价值评估的系统，其特征在于，包括：
    一输入模块，将待评估商标的基本信息进行录入，待评估商标的基本信息为商标名称和设计要素系列资料；
    一数据库，从数据库中获取建立维度的资料，建立维度的资料包括侵权审判、相同或相近似类别商标的交易金额、授权时间、该类别商标的总体数量、驳回率；
    一大数据判断模块和一人工智能模块，分析出根据大数据判断商标的价值，对于跟交易金额相关的各变量赋予一定权重，再加权得出基本值，对于跟交易金额无关的各变量加权形成系数大于1的因子1，相似商标的数量形成系数小于1的因子2，待评估商标价值等于基本值*因子1*因子2；
    一输出模块，将待评估商标的最终价值评估报告输出。
  8. 如权利要求7所述的基于大数据的商标价值评估的系统，其特征在于：提取待评估商标的图形，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，如果有,则判断在先已注册的商标或正在申请中的商标是否有效，如果是有效,则判断与商品是否近 似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似,则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似，所述相近似的判断由人工智能模块进行判断，所述不近似的判断由大数据模块进行判断。
  9. 如权利要求7所述的基于大数据的商标价值评估的系统，其特征在于：提取待评估商标的文字，判断是否在同一小类有相同的在先已注册的商标或正在申请中的商标，所述相同的判断按照商标法、实施细则及审查指南规定进行判断，如果有，则判断在先已注册的商标或正在申请中的商标是否有效，如果有效，则判断与商品是否近似，如果近似，则给出申请成功可能性低的建议并生成报告，如果不近似，则给出申请成功可能性高的建议并生成报告，如果是无效，则判断是否超过预定时间，如果超过,则给出申请成功可能性低的建议并生成报告，如果不超过,则给出申请成功可能性高的建议并生成报告，如果无,则判断待评估商标与在先已注册的商标或正在申请中的商标是否相近似，所述不近似的判断和所述相近似的判断均由大数据模块进行判断。
  10. 如权利要求7所述的基于大数据的商标价值评估的系统，其特征在于：建立维度进一步包括该行业上市公司的数量和该行业上市公司的利润率；建立维度进一步包括该行业注册公司的数量及速率、电商的数量及速率；侵权审判为提取该商品涉及侵权纠纷的在先已注册的商标或正在申请中的商标的类别、城市、国家或地区，涉及侵权纠纷的在先已注册的商标或正在申请中的商标的金钱数额和纠纷当事人；权重可动态调整，对新兴行业可以进行权重调整，新兴行业判断标准为三种：第一种为该领域的公司注册数量，第二种为该领域的公司融资数量，第三种为该领域的公司注册数量和融资数量。
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